The network relationship between electricity station owner and top 10 CA cities.

Electricity Consumption in LA with Time Series


        County        LA
1         2019  19562.55
2         2018  20516.12
3         2017  20663.27
4         2016  20287.96
5         2015  20432.53
6         2014  20742.77
7         2013  20611.29
8         2012  21076.22
9         2011  20064.62
10        2010  19721.28
11        2009  20590.38
12        2008  21115.74
13        2007  20536.16
14        2006  20377.01
15        2005  19711.01
16        2004  19507.01
17        2003  19056.04
18        2002  17917.34
19        2001  18212.58
20        2000  18891.64
21        1999  17665.12
22        1998  17234.63
23        1997  17578.66
24        1996  16322.05
25        1995  16823.63
26        1994  16065.58
27        1993  15759.08
28        1992  16377.79
29        1991  16310.88
30        1990  16960.72
31 Total Usage 566691.67

Los Angeles Housing Prices Distribution With Population.

The distribution of housing price amoung ocean proximity in LA

WordCloud of LA House and Electricity Article

ABOUT


About

---
title: "Explore Housing and Electricity Relationship in LA"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
```


```{r}
library(tidyverse)
library(ggplot2)
library(scales)
library(viridis)  
library(igraph)
library(plotly)
library(sp)
library(maps)
library(maptools)
library(wordcloud2) 
library(tm)
library(SnowballC)
library(wordcloud)
library(RColorBrewer)
Sys.setenv(MAPBOX_TOKEN = 11122223333444)
```


```{r}
electricity = read.csv("Electricity.csv")

groupbys <- electricity %>%
  select('Owner', 'County', 'Highest_kV') %>%
  group_by(County) %>%
  drop_na()

groupbys['weight'] <- as.numeric(factor(groupbys[['Highest_kV']]))
groupbys <- groupbys %>%
  select('Owner', 'County', 'weight')

groupbys <- groupbys %>%
  filter(County %in% c('Los Angeles', 'San Diego', 
                       'Orange', 'Riverside', 'San Bernardino',
                       'Santa Clara', 'Sacramento', 'San Francisco',
                       'Kern', 'Fresno'))

graphdf <- groupbys %>%
  group_by(Owner, County) %>%
  summarize(total_weight = sum(weight))

graphdf <- graphdf %>% drop_na()

```


### The network relationship between electricity station owner and top 10 CA cities.


```{r}
graphdf1 <- graph_from_data_frame(graphdf, directed = TRUE)

V(graphdf1)[1:10]$colour = 'gray'
V(graphdf1)[10]$colour = 'chocolate'
V(graphdf1)[11]$colour = 'chocolate1'
V(graphdf1)[12]$colour = 'chocolate2'
V(graphdf1)[13]$colour = 'chocolate3'
V(graphdf1)[14]$colour = 'coral'
V(graphdf1)[15]$colour = 'coral1'
V(graphdf1)[16]$colour = 'coral2'
V(graphdf1)[17]$colour = 'coral3'
V(graphdf1)[18]$colour = 'burlywood1'
V(graphdf1)[19]$colour = 'burlywood2'

E(graphdf1)$weight <- 2*scale(graphdf$total_weight) + 3

par(mar = c(0.3, 0.3, 1, 0.3))
plot(graphdf1, 
   layout=layout_in_circle,
   edge.arrow.size = 0.3,
   vertex.size=20, 
   edge.width = E(graphdf1)$weight,
   vertex.color = adjustcolor(V(graphdf1)$colour,alpha.f = .8))

```



```{r}
consumption <- read.csv("Consumption.csv")

```

### Electricity Consumption in LA with Time Series

```{r}
fig <- plot_ly(
  type = "scatter",
  x = as.Date(consumption$County, format = "%Y"),
  y = consumption$LA,
  name = 'Electricity Consumption in LA',
  mode = "markers",
)

fig <- fig %>%
  layout(
    title = "Electricity Consumption in LA"
  )

fig
```

***

```{r}
consumption
```


```{r}
housing = read_csv("housing.csv")

latlong2county <- function(pointsDF) {
    # Prepare SpatialPolygons object with one SpatialPolygon
    # per county
    counties <- map('county', fill=TRUE, col="transparent", plot=FALSE)
    IDs <- sapply(strsplit(counties$names, ":"), function(x) x[1])
    counties_sp <- map2SpatialPolygons(counties, IDs=IDs,
                     proj4string=CRS("+proj=longlat +datum=WGS84"))

    # Convert pointsDF to a SpatialPoints object 
    pointsSP <- SpatialPoints(pointsDF[1:2], 
                    proj4string=CRS("+proj=longlat +datum=WGS84"))

    # Use 'over' to get _indices_ of the Polygons object containing each point 
    indices <- over(pointsSP, counties_sp)

    # Return the county names of the Polygons object containing each point
    countyNames <- sapply(counties_sp@polygons, function(x) x@ID)
    countyNames[indices]
}

# Test the function using points in Wisconsin and Oregon.
testPoints <- data.frame(x = c(-90, -120), y = c(44, 44))

housing['county'] = latlong2county(housing)

housing <- housing %>%
  filter(county %in% c("california,los angeles"))

housing$Category[housing$median_house_value<=100000] = '<100K'
housing$Category[housing$median_house_value>100000 & housing$median_house_value<=200000] = '100K~200K'
housing$Category[housing$median_house_value>200000 & housing$median_house_value<=300000] = '200K~300K'
housing$Category[housing$median_house_value>300000 & housing$median_house_value<=400000] = '300K~400K'
housing$Category[housing$median_house_value>400000 ] = '>400K'

```

### Los Angeles Housing Prices Distribution With Population.


```{r}
options(warn=-1)

plot_map = ggplot(housing, 
                  aes(x = longitude, y = latitude, color = median_house_value, 
                      hma = housing_median_age, tr = total_rooms, tb = total_bedrooms,
                      hh = households, mi = median_income)) +
              geom_point(aes(size = population), alpha = 0.6) +
              xlab("Longitude") +
              ylab("Latitude") +
              ggtitle("Los Angeles Housing Price Distribution") +
              theme(plot.title = element_text(hjust = 0.5)) +
              scale_color_viridis(option = "D", labels = comma) + 
              labs(color = "Median House Value (in $USD)", size = "Population")
plot_map
```

### The distribution of housing price amoung ocean proximity in LA

```{r}
fig <- plot_ly(housing, x = ~ocean_proximity, color = ~Category) %>%
  add_histogram()

fig <- fig %>%
  layout(
    title = "The distribution of housing price amoung ocean proximity in LA"
  )

fig
```


### WordCloud of LA House and Electricity Article

```{r}
text <- readLines('LA_housing_overview.txt')

text <- sapply(text,function(row) iconv(row, "latin1", "ASCII", sub=""))

corpus = VCorpus(VectorSource(text))
corpus = tm_map(corpus, content_transformer(tolower))
corpus = tm_map(corpus, removeNumbers)
corpus = tm_map(corpus, removePunctuation)
corpus = tm_map(corpus, removeWords, stopwords())
#corpus = tm_map(corpus, stemDocument)
corpus = tm_map(corpus, stripWhitespace)


```


```{r}
dtm = DocumentTermMatrix(corpus)
dtm = removeSparseTerms(dtm, 0.9999)
dataset = as.matrix(dtm)
v = sort(colSums(dataset),decreasing=TRUE)
myNames = names(v)
d = data.frame(word=myNames,freq=v)
pal2 <- brewer.pal(8,"Dark2")
wordcloud(d$word, colors = pal2,scale=c(4,.6), random.color=FALSE, d$freq, min.freq=15, max.words=200, random.order=FALSE)
```


### ABOUT

*** 
About